Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations20000
Missing cells5828
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory168.0 B

Variable types

Numeric16
Categorical4

Alerts

efficiency is highly overall correlated with irradianceHigh correlation
error_code is highly overall correlated with has_errorHigh correlation
has_error is highly overall correlated with error_codeHigh correlation
irradiance is highly overall correlated with efficiency and 1 other fieldsHigh correlation
module_temperature is highly overall correlated with temperatureHigh correlation
power_output is highly overall correlated with voltageHigh correlation
soiled_irradiance is highly overall correlated with irradiance and 1 other fieldsHigh correlation
soiling_ratio is highly overall correlated with soiled_irradianceHigh correlation
temperature is highly overall correlated with module_temperatureHigh correlation
voltage is highly overall correlated with power_outputHigh correlation
power_output has 1925 (9.6%) missing values Missing
temp_diff has 1947 (9.7%) missing values Missing
soiled_irradiance has 1956 (9.8%) missing values Missing
temperature has 389 (1.9%) zeros Zeros
maintenance_count has 349 (1.7%) zeros Zeros
voltage has 5158 (25.8%) zeros Zeros
efficiency has 631 (3.2%) zeros Zeros
power_output has 4901 (24.5%) zeros Zeros

Reproduction

Analysis started2025-06-08 14:29:54.631514
Analysis finished2025-06-08 14:30:20.124580
Duration25.49 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

temperature
Real number (ℝ)

High correlation  Zeros 

Distinct18579
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.059378
Minimum0
Maximum147.39417
Zeros389
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2025-06-08T20:00:20.346210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.2925856
Q117.311474
median24.720345
Q332.360468
95-th percentile44.485509
Maximum147.39417
Range147.39417
Interquartile range (IQR)15.048994

Descriptive statistics

Standard deviation12.196201
Coefficient of variation (CV)0.48669209
Kurtosis7.5546318
Mean25.059378
Median Absolute Deviation (MAD)7.5213517
Skewness1.0293426
Sum501187.57
Variance148.74732
MonotonicityNot monotonic
2025-06-08T20:00:20.446779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.72034535 1002
 
5.0%
0 389
 
1.9%
60 32
 
0.2%
100 2
 
< 0.1%
7.817315336 1
 
< 0.1%
24.78572741 1
 
< 0.1%
44.00745253 1
 
< 0.1%
34.17485879 1
 
< 0.1%
12.36602051 1
 
< 0.1%
16.18000277 1
 
< 0.1%
Other values (18569) 18569
92.8%
ValueCountFrequency (%)
0 389
1.9%
0.02038933015 1
 
< 0.1%
0.03874599618 1
 
< 0.1%
0.07110273443 1
 
< 0.1%
0.08827461079 1
 
< 0.1%
0.08994888816 1
 
< 0.1%
0.09553615557 1
 
< 0.1%
0.1110309926 1
 
< 0.1%
0.1240578039 1
 
< 0.1%
0.1250704593 1
 
< 0.1%
ValueCountFrequency (%)
147.394168 1
< 0.1%
145.858047 1
< 0.1%
139.185993 1
< 0.1%
137.4114329 1
< 0.1%
137.0362088 1
< 0.1%
136.0511058 1
< 0.1%
134.8421121 1
< 0.1%
133.6369589 1
< 0.1%
133.549199 1
< 0.1%
133.4728321 1
< 0.1%

irradiance
Real number (ℝ)

High correlation 

Distinct19013
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501.19399
Minimum-597.27865
Maximum1537.8103
Zeros0
Zeros (%)0.0%
Negative424
Negative (%)2.1%
Memory size312.5 KiB
2025-06-08T20:00:20.548307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-597.27865
5-th percentile95.432626
Q1343.00983
median499.65473
Q3658.31505
95-th percentile909.98485
Maximum1537.8103
Range2135.089
Interquartile range (IQR)315.30522

Descriptive statistics

Standard deviation244.65658
Coefficient of variation (CV)0.48814747
Kurtosis0.14928078
Mean501.19399
Median Absolute Deviation (MAD)157.79223
Skewness0.00044978994
Sum10023880
Variance59856.84
MonotonicityNot monotonic
2025-06-08T20:00:20.636857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
499.6547299 988
 
4.9%
453.7844091 1
 
< 0.1%
416.2050576 1
 
< 0.1%
396.6924209 1
 
< 0.1%
174.4551233 1
 
< 0.1%
735.1411791 1
 
< 0.1%
342.9707098 1
 
< 0.1%
128.6610515 1
 
< 0.1%
570.7876392 1
 
< 0.1%
179.3519999 1
 
< 0.1%
Other values (19003) 19003
95.0%
ValueCountFrequency (%)
-597.2786462 1
< 0.1%
-471.9510854 1
< 0.1%
-425.0228898 1
< 0.1%
-412.1032063 1
< 0.1%
-396.2451402 1
< 0.1%
-382.3024883 1
< 0.1%
-361.1460124 1
< 0.1%
-352.1780191 1
< 0.1%
-344.2743734 1
< 0.1%
-315.8166756 1
< 0.1%
ValueCountFrequency (%)
1537.810349 1
< 0.1%
1506.456011 1
< 0.1%
1363.511601 1
< 0.1%
1358.981904 1
< 0.1%
1323.463527 1
< 0.1%
1310.314769 1
< 0.1%
1308.373416 1
< 0.1%
1302.040228 1
< 0.1%
1300.060745 1
< 0.1%
1299.735477 1
< 0.1%

humidity
Real number (ℝ)

Distinct19873
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.067173
Minimum0.010714034
Maximum99.995202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2025-06-08T20:00:20.807401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.010714034
5-th percentile5.0184311
Q125.432917
median50.224152
Q374.373083
95-th percentile95.127648
Maximum99.995202
Range99.984488
Interquartile range (IQR)48.940165

Descriptive statistics

Standard deviation28.618356
Coefficient of variation (CV)0.57159919
Kurtosis-1.1634915
Mean50.067173
Median Absolute Deviation (MAD)24.463459
Skewness0.0002158895
Sum1001343.5
Variance819.01029
MonotonicityNot monotonic
2025-06-08T20:00:20.900964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.22415155 128
 
0.6%
41.24308671 1
 
< 0.1%
1.359648277 1
 
< 0.1%
3.729031261 1
 
< 0.1%
48.8900812 1
 
< 0.1%
77.51469164 1
 
< 0.1%
7.304592735 1
 
< 0.1%
34.80091211 1
 
< 0.1%
13.96070257 1
 
< 0.1%
71.82899585 1
 
< 0.1%
Other values (19863) 19863
99.3%
ValueCountFrequency (%)
0.01071403363 1
< 0.1%
0.01568582896 1
< 0.1%
0.01762236622 1
< 0.1%
0.02207489924 1
< 0.1%
0.02220945244 1
< 0.1%
0.02887818179 1
< 0.1%
0.03908010678 1
< 0.1%
0.040578856 1
< 0.1%
0.04399456181 1
< 0.1%
0.0455158809 1
< 0.1%
ValueCountFrequency (%)
99.99520178 1
< 0.1%
99.99493296 1
< 0.1%
99.99153925 1
< 0.1%
99.99066612 1
< 0.1%
99.98636146 1
< 0.1%
99.9830001 1
< 0.1%
99.98189501 1
< 0.1%
99.97130837 1
< 0.1%
99.96720424 1
< 0.1%
99.96533835 1
< 0.1%

panel_age
Real number (ℝ)

Distinct18989
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.50915
Minimum0.001264172
Maximum34.998379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2025-06-08T20:00:20.989591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.001264172
5-th percentile1.886391
Q19.2381133
median17.497731
Q325.832048
95-th percentile33.18721
Maximum34.998379
Range34.997115
Interquartile range (IQR)16.593934

Descriptive statistics

Standard deviation9.839019
Coefficient of variation (CV)0.56193584
Kurtosis-1.1016902
Mean17.50915
Median Absolute Deviation (MAD)8.2941382
Skewness0.0092791434
Sum350183.01
Variance96.806295
MonotonicityNot monotonic
2025-06-08T20:00:21.085189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.49773098 1012
 
5.1%
28.89179141 1
 
< 0.1%
25.11585157 1
 
< 0.1%
11.08457842 1
 
< 0.1%
23.98348094 1
 
< 0.1%
24.50062195 1
 
< 0.1%
27.49913641 1
 
< 0.1%
24.28500556 1
 
< 0.1%
6.504700849 1
 
< 0.1%
20.0731336 1
 
< 0.1%
Other values (18979) 18979
94.9%
ValueCountFrequency (%)
0.001264172048 1
< 0.1%
0.001338315138 1
< 0.1%
0.001402704032 1
< 0.1%
0.001957725218 1
< 0.1%
0.005754128572 1
< 0.1%
0.009278599011 1
< 0.1%
0.009488194128 1
< 0.1%
0.009687198284 1
< 0.1%
0.01284995761 1
< 0.1%
0.01563621786 1
< 0.1%
ValueCountFrequency (%)
34.99837895 1
< 0.1%
34.99771455 1
< 0.1%
34.99354334 1
< 0.1%
34.99259254 1
< 0.1%
34.99135502 1
< 0.1%
34.99023835 1
< 0.1%
34.98975037 1
< 0.1%
34.98673764 1
< 0.1%
34.98465846 1
< 0.1%
34.98374268 1
< 0.1%

maintenance_count
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.01145
Minimum0
Maximum15
Zeros349
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2025-06-08T20:00:21.158956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile8
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9501818
Coefficient of variation (CV)0.48615383
Kurtosis0.43825887
Mean4.01145
Median Absolute Deviation (MAD)1
Skewness0.52556644
Sum80229
Variance3.8032091
MonotonicityNot monotonic
2025-06-08T20:00:21.224425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
4 4730
23.6%
3 3744
18.7%
5 2988
14.9%
2 2785
13.9%
6 1948
9.7%
1 1331
 
6.7%
7 1108
 
5.5%
8 593
 
3.0%
0 349
 
1.7%
9 271
 
1.4%
Other values (5) 153
 
0.8%
ValueCountFrequency (%)
0 349
 
1.7%
1 1331
 
6.7%
2 2785
13.9%
3 3744
18.7%
4 4730
23.6%
5 2988
14.9%
6 1948
9.7%
7 1108
 
5.5%
8 593
 
3.0%
9 271
 
1.4%
ValueCountFrequency (%)
15 1
 
< 0.1%
13 3
 
< 0.1%
12 14
 
0.1%
11 37
 
0.2%
10 98
 
0.5%
9 271
 
1.4%
8 593
 
3.0%
7 1108
 
5.5%
6 1948
9.7%
5 2988
14.9%

soiling_ratio
Real number (ℝ)

High correlation 

Distinct18991
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69881786
Minimum0.40014869
Maximum0.99994909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2025-06-08T20:00:21.307058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.40014869
5-th percentile0.43212855
Q10.55853629
median0.69766345
Q30.83956868
95-th percentile0.96733513
Maximum0.99994909
Range0.5998004
Interquartile range (IQR)0.28103239

Descriptive statistics

Standard deviation0.16783844
Coefficient of variation (CV)0.2401748
Kurtosis-1.0956318
Mean0.69881786
Median Absolute Deviation (MAD)0.14051645
Skewness0.011556056
Sum13976.357
Variance0.028169743
MonotonicityNot monotonic
2025-06-08T20:00:21.401700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6976634526 1010
 
5.1%
0.7337016162 1
 
< 0.1%
0.9647902101 1
 
< 0.1%
0.7573964748 1
 
< 0.1%
0.6276416635 1
 
< 0.1%
0.9386984672 1
 
< 0.1%
0.6198386979 1
 
< 0.1%
0.9719221731 1
 
< 0.1%
0.8031991409 1
 
< 0.1%
0.4794558186 1
 
< 0.1%
Other values (18981) 18981
94.9%
ValueCountFrequency (%)
0.4001486939 1
< 0.1%
0.4002659362 1
< 0.1%
0.4002668671 1
< 0.1%
0.4003243192 1
< 0.1%
0.4003408622 1
< 0.1%
0.4003755211 1
< 0.1%
0.4003761213 1
< 0.1%
0.400397365 1
< 0.1%
0.4004507242 1
< 0.1%
0.400472237 1
< 0.1%
ValueCountFrequency (%)
0.9999490901 1
< 0.1%
0.9998690605 1
< 0.1%
0.9998270665 1
< 0.1%
0.9998085441 1
< 0.1%
0.9998060657 1
< 0.1%
0.9997940923 1
< 0.1%
0.9997751783 1
< 0.1%
0.9997078538 1
< 0.1%
0.9996710295 1
< 0.1%
0.9996626083 1
< 0.1%

voltage
Real number (ℝ)

High correlation  Zeros 

Distinct13850
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.049007
Minimum0
Maximum494.27902
Zeros5158
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2025-06-08T20:00:21.502407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12.350138
Q325.81905
95-th percentile46.569919
Maximum494.27902
Range494.27902
Interquartile range (IQR)25.81905

Descriptive statistics

Standard deviation17.459742
Coefficient of variation (CV)1.0879017
Kurtosis70.951148
Mean16.049007
Median Absolute Deviation (MAD)12.350138
Skewness4.1882241
Sum320980.15
Variance304.8426
MonotonicityNot monotonic
2025-06-08T20:00:21.601054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5158
 
25.8%
12.35013818 994
 
5.0%
0.7063475839 1
 
< 0.1%
13.22852319 1
 
< 0.1%
19.97657333 1
 
< 0.1%
1.670913424 1
 
< 0.1%
34.88895414 1
 
< 0.1%
12.14936529 1
 
< 0.1%
19.26342838 1
 
< 0.1%
8.036343408 1
 
< 0.1%
Other values (13840) 13840
69.2%
ValueCountFrequency (%)
0 5158
25.8%
0.009256287964 1
 
< 0.1%
0.01063779906 1
 
< 0.1%
0.01542910637 1
 
< 0.1%
0.01590607587 1
 
< 0.1%
0.02887941189 1
 
< 0.1%
0.02985059589 1
 
< 0.1%
0.03328751911 1
 
< 0.1%
0.034149682 1
 
< 0.1%
0.03956874607 1
 
< 0.1%
ValueCountFrequency (%)
494.2790158 1
< 0.1%
397.6997191 1
< 0.1%
392.3067753 1
< 0.1%
326.6993684 1
< 0.1%
287.8654762 1
< 0.1%
286.6203666 1
< 0.1%
283.2814161 1
< 0.1%
257.4017329 1
< 0.1%
255.3379308 1
< 0.1%
242.6205605 1
< 0.1%

current
Real number (ℝ)

Distinct19023
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7058254
Minimum5.3963352 × 10-5
Maximum7.3155969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2025-06-08T20:00:21.686888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.3963352 × 10-5
5-th percentile0.1643192
Q10.81355506
median1.5584131
Q32.4105315
95-th percentile3.7886999
Maximum7.3155969
Range7.3155429
Interquartile range (IQR)1.5969764

Descriptive statistics

Standard deviation1.1249346
Coefficient of variation (CV)0.65946643
Kurtosis0.23348612
Mean1.7058254
Median Absolute Deviation (MAD)0.79145915
Skewness0.70931772
Sum34116.509
Variance1.2654779
MonotonicityNot monotonic
2025-06-08T20:00:21.786501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.558413082 978
 
4.9%
1.913165343 1
 
< 0.1%
2.355551847 1
 
< 0.1%
0.9409516138 1
 
< 0.1%
1.385305134 1
 
< 0.1%
1.270745864 1
 
< 0.1%
4.017683919 1
 
< 0.1%
1.963787043 1
 
< 0.1%
0.2414732915 1
 
< 0.1%
4.191800244 1
 
< 0.1%
Other values (19013) 19013
95.1%
ValueCountFrequency (%)
5.396335164 × 10-51
< 0.1%
0.0003416714692 1
< 0.1%
0.0003524447036 1
< 0.1%
0.001252890056 1
< 0.1%
0.001329042838 1
< 0.1%
0.001475093039 1
< 0.1%
0.00159835169 1
< 0.1%
0.00237860516 1
< 0.1%
0.002625057824 1
< 0.1%
0.00287182547 1
< 0.1%
ValueCountFrequency (%)
7.315596866 1
< 0.1%
7.308433881 1
< 0.1%
7.121201976 1
< 0.1%
6.770585665 1
< 0.1%
6.652854124 1
< 0.1%
6.544965796 1
< 0.1%
6.540699694 1
< 0.1%
6.41024259 1
< 0.1%
6.402262011 1
< 0.1%
6.343704404 1
< 0.1%

module_temperature
Real number (ℝ)

High correlation 

Distinct18965
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.920572
Minimum0
Maximum65
Zeros20
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2025-06-08T20:00:21.882061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.119053
Q122.001083
median29.857669
Q337.596789
95-th percentile49.995551
Maximum65
Range65
Interquartile range (IQR)15.595706

Descriptive statistics

Standard deviation11.825217
Coefficient of variation (CV)0.39522027
Kurtosis-0.12641939
Mean29.920572
Median Absolute Deviation (MAD)7.8010554
Skewness0.091010805
Sum598411.45
Variance139.83575
MonotonicityNot monotonic
2025-06-08T20:00:21.978679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.8576686 978
 
4.9%
65 40
 
0.2%
0 20
 
0.1%
11.42621373 1
 
< 0.1%
20.26691589 1
 
< 0.1%
29.31204572 1
 
< 0.1%
47.56434183 1
 
< 0.1%
19.39229988 1
 
< 0.1%
57.72043617 1
 
< 0.1%
30.38201988 1
 
< 0.1%
Other values (18955) 18955
94.8%
ValueCountFrequency (%)
0 20
0.1%
0.05347103975 1
 
< 0.1%
0.1139519866 1
 
< 0.1%
0.1326711725 1
 
< 0.1%
0.1677183119 1
 
< 0.1%
0.2016679133 1
 
< 0.1%
0.2339604914 1
 
< 0.1%
0.2935541685 1
 
< 0.1%
0.3139670484 1
 
< 0.1%
0.3282753724 1
 
< 0.1%
ValueCountFrequency (%)
65 40
0.2%
64.9686027 1
 
< 0.1%
64.82239725 1
 
< 0.1%
64.73937456 1
 
< 0.1%
64.58430498 1
 
< 0.1%
64.43004806 1
 
< 0.1%
64.28942464 1
 
< 0.1%
64.26264679 1
 
< 0.1%
64.21330929 1
 
< 0.1%
64.07605657 1
 
< 0.1%

cloud_coverage
Real number (ℝ)

Distinct18960
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.294016
Minimum0.00024366652
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2025-06-08T20:00:22.155756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.00024366652
5-th percentile5.1418227
Q126.548853
median49.704133
Q373.798123
95-th percentile94.764068
Maximum1000
Range999.99976
Interquartile range (IQR)47.24927

Descriptive statistics

Standard deviation47.235208
Coefficient of variation (CV)0.92087172
Kurtosis257.69703
Mean51.294016
Median Absolute Deviation (MAD)23.631875
Skewness12.932404
Sum1025880.3
Variance2231.1649
MonotonicityNot monotonic
2025-06-08T20:00:22.249595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.70413334 1010
 
5.1%
1000 32
 
0.2%
33.96265284 1
 
< 0.1%
61.45526676 1
 
< 0.1%
70.25809504 1
 
< 0.1%
31.88822235 1
 
< 0.1%
87.82924299 1
 
< 0.1%
49.93000684 1
 
< 0.1%
38.57445515 1
 
< 0.1%
25.82702298 1
 
< 0.1%
Other values (18950) 18950
94.8%
ValueCountFrequency (%)
0.0002436665158 1
< 0.1%
0.002634590726 1
< 0.1%
0.009258186137 1
< 0.1%
0.01463328932 1
< 0.1%
0.01785980957 1
< 0.1%
0.02235329255 1
< 0.1%
0.02829875029 1
< 0.1%
0.06932060787 1
< 0.1%
0.07001427042 1
< 0.1%
0.07458108811 1
< 0.1%
ValueCountFrequency (%)
1000 32
0.2%
99.99926664 1
 
< 0.1%
99.99504464 1
 
< 0.1%
99.99330433 1
 
< 0.1%
99.99081415 1
 
< 0.1%
99.9849398 1
 
< 0.1%
99.98281855 1
 
< 0.1%
99.9758756 1
 
< 0.1%
99.97567072 1
 
< 0.1%
99.97114218 1
 
< 0.1%

wind_speed
Real number (ℝ)

Distinct19881
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4131235
Minimum0.0012768908
Maximum14.999448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2025-06-08T20:00:22.340051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0012768908
5-th percentile0.70694356
Q13.6910655
median7.3960906
Q311.126792
95-th percentile14.194972
Maximum14.999448
Range14.998172
Interquartile range (IQR)7.435726

Descriptive statistics

Standard deviation4.3169597
Coefficient of variation (CV)0.58234018
Kurtosis-1.1892225
Mean7.4131235
Median Absolute Deviation (MAD)3.7132941
Skewness0.013567669
Sum148262.47
Variance18.636141
MonotonicityNot monotonic
2025-06-08T20:00:22.435291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.396090601 120
 
0.6%
1.814399756 1
 
< 0.1%
8.736258932 1
 
< 0.1%
0.5226838408 1
 
< 0.1%
8.541404096 1
 
< 0.1%
3.593830062 1
 
< 0.1%
8.321732515 1
 
< 0.1%
1.400463906 1
 
< 0.1%
14.33082777 1
 
< 0.1%
0.7985304352 1
 
< 0.1%
Other values (19871) 19871
99.4%
ValueCountFrequency (%)
0.001276890801 1
< 0.1%
0.003187471865 1
< 0.1%
0.003230688829 1
< 0.1%
0.004040162292 1
< 0.1%
0.00530049955 1
< 0.1%
0.005899759621 1
< 0.1%
0.006134983462 1
< 0.1%
0.007067411845 1
< 0.1%
0.007375100913 1
< 0.1%
0.008568452659 1
< 0.1%
ValueCountFrequency (%)
14.99944848 1
< 0.1%
14.99871886 1
< 0.1%
14.99827932 1
< 0.1%
14.99825251 1
< 0.1%
14.99820857 1
< 0.1%
14.99707577 1
< 0.1%
14.99668847 1
< 0.1%
14.99638757 1
< 0.1%
14.99622288 1
< 0.1%
14.99596203 1
< 0.1%

pressure
Real number (ℝ)

Distinct19865
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1012.9808
Minimum970.08737
Maximum1052.8657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2025-06-08T20:00:22.537771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum970.08737
5-th percentile996.54127
Q11006.2367
median1012.9061
Q31019.7434
95-th percentile1029.4098
Maximum1052.8657
Range82.77835
Interquartile range (IQR)13.506724

Descriptive statistics

Standard deviation10.012282
Coefficient of variation (CV)0.0098839798
Kurtosis-0.001467852
Mean1012.9808
Median Absolute Deviation (MAD)6.7440437
Skewness0.0034961003
Sum20259616
Variance100.24578
MonotonicityNot monotonic
2025-06-08T20:00:22.631778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1012.906121 136
 
0.7%
1010.922654 1
 
< 0.1%
1021.846663 1
 
< 0.1%
1008.555958 1
 
< 0.1%
1006.95788 1
 
< 0.1%
1016.00508 1
 
< 0.1%
1030.49718 1
 
< 0.1%
1012.800375 1
 
< 0.1%
1001.562627 1
 
< 0.1%
1008.750482 1
 
< 0.1%
Other values (19855) 19855
99.3%
ValueCountFrequency (%)
970.0873651 1
< 0.1%
970.4472023 1
< 0.1%
976.7667256 1
< 0.1%
976.9703217 1
< 0.1%
977.2483553 1
< 0.1%
977.7651052 1
< 0.1%
978.410813 1
< 0.1%
978.6249246 1
< 0.1%
979.5182422 1
< 0.1%
979.9286736 1
< 0.1%
ValueCountFrequency (%)
1052.865715 1
< 0.1%
1049.237906 1
< 0.1%
1048.377086 1
< 0.1%
1048.332323 1
< 0.1%
1047.463424 1
< 0.1%
1046.889941 1
< 0.1%
1046.401373 1
< 0.1%
1046.093602 1
< 0.1%
1045.75245 1
< 0.1%
1045.494797 1
< 0.1%

string_id
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
3.0
5080 
1.0
5014 
2.0
5004 
0.0
4902 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row3.0
3rd row2.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 5080
25.4%
1.0 5014
25.1%
2.0 5004
25.0%
0.0 4902
24.5%

Length

2025-06-08T20:00:22.719494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T20:00:22.776116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 5080
25.4%
1.0 5014
25.1%
2.0 5004
25.0%
0.0 4902
24.5%

Most occurring characters

ValueCountFrequency (%)
0 24902
41.5%
. 20000
33.3%
3 5080
 
8.5%
1 5014
 
8.4%
2 5004
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 24902
41.5%
. 20000
33.3%
3 5080
 
8.5%
1 5014
 
8.4%
2 5004
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 24902
41.5%
. 20000
33.3%
3 5080
 
8.5%
1 5014
 
8.4%
2 5004
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 24902
41.5%
. 20000
33.3%
3 5080
 
8.5%
1 5014
 
8.4%
2 5004
 
8.3%

error_code
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0.0
5977 
3.0
5912 
1.0
4100 
2.0
4011 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row0.0
3rd row0.0
4th row3.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5977
29.9%
3.0 5912
29.6%
1.0 4100
20.5%
2.0 4011
20.1%

Length

2025-06-08T20:00:22.847723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T20:00:22.900707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5977
29.9%
3.0 5912
29.6%
1.0 4100
20.5%
2.0 4011
20.1%

Most occurring characters

ValueCountFrequency (%)
0 25977
43.3%
. 20000
33.3%
3 5912
 
9.9%
1 4100
 
6.8%
2 4011
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25977
43.3%
. 20000
33.3%
3 5912
 
9.9%
1 4100
 
6.8%
2 4011
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25977
43.3%
. 20000
33.3%
3 5912
 
9.9%
1 4100
 
6.8%
2 4011
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25977
43.3%
. 20000
33.3%
3 5912
 
9.9%
1 4100
 
6.8%
2 4011
 
6.7%

installation_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
3.0
5067 
2.0
5028 
1.0
4990 
0.0
4915 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row2.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 5067
25.3%
2.0 5028
25.1%
1.0 4990
24.9%
0.0 4915
24.6%

Length

2025-06-08T20:00:22.965436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T20:00:23.018331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 5067
25.3%
2.0 5028
25.1%
1.0 4990
24.9%
0.0 4915
24.6%

Most occurring characters

ValueCountFrequency (%)
0 24915
41.5%
. 20000
33.3%
3 5067
 
8.4%
2 5028
 
8.4%
1 4990
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 24915
41.5%
. 20000
33.3%
3 5067
 
8.4%
2 5028
 
8.4%
1 4990
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 24915
41.5%
. 20000
33.3%
3 5067
 
8.4%
2 5028
 
8.4%
1 4990
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 24915
41.5%
. 20000
33.3%
3 5067
 
8.4%
2 5028
 
8.4%
1 4990
 
8.3%

efficiency
Real number (ℝ)

High correlation  Zeros 

Distinct19370
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51026029
Minimum0
Maximum0.98706569
Zeros631
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2025-06-08T20:00:23.101082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.32449066
Q10.44561263
median0.51570943
Q30.59032438
95-th percentile0.71235252
Maximum0.98706569
Range0.98706569
Interquartile range (IQR)0.14471174

Descriptive statistics

Standard deviation0.14041959
Coefficient of variation (CV)0.27519209
Kurtosis3.7308241
Mean0.51026029
Median Absolute Deviation (MAD)0.07230852
Skewness-1.1557189
Sum10205.206
Variance0.019717662
MonotonicityNot monotonic
2025-06-08T20:00:23.205664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 631
 
3.2%
0.3418737229 1
 
< 0.1%
0.3761270807 1
 
< 0.1%
0.5371956997 1
 
< 0.1%
0.4668498196 1
 
< 0.1%
0.5386002931 1
 
< 0.1%
0.3797708453 1
 
< 0.1%
0.4755599593 1
 
< 0.1%
0.4453078026 1
 
< 0.1%
0.5328767948 1
 
< 0.1%
Other values (19360) 19360
96.8%
ValueCountFrequency (%)
0 631
3.2%
0.1 1
 
< 0.1%
0.1326277174 1
 
< 0.1%
0.146358205 1
 
< 0.1%
0.1620733005 1
 
< 0.1%
0.1689094674 1
 
< 0.1%
0.1746869306 1
 
< 0.1%
0.1857529344 1
 
< 0.1%
0.1925767973 1
 
< 0.1%
0.1935003387 1
 
< 0.1%
ValueCountFrequency (%)
0.9870656933 1
< 0.1%
0.9753445706 1
< 0.1%
0.9512654882 1
< 0.1%
0.9458545172 1
< 0.1%
0.9450777667 1
< 0.1%
0.9419998125 1
< 0.1%
0.9404465828 1
< 0.1%
0.940072698 1
< 0.1%
0.9378884848 1
< 0.1%
0.9371407315 1
< 0.1%

power_output
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct13175
Distinct (%)72.9%
Missing1925
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean30.076299
Minimum0
Maximum669.18064
Zeros4901
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2025-06-08T20:00:23.296224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12.740142
Q343.3739
95-th percentile116.11693
Maximum669.18064
Range669.18064
Interquartile range (IQR)43.3739

Descriptive statistics

Standard deviation43.731186
Coefficient of variation (CV)1.4540082
Kurtosis16.818697
Mean30.076299
Median Absolute Deviation (MAD)12.740142
Skewness2.9329831
Sum543629.1
Variance1912.4166
MonotonicityNot monotonic
2025-06-08T20:00:23.385771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4901
 
24.5%
44.470168 1
 
< 0.1%
15.41585177 1
 
< 0.1%
1.505516903 1
 
< 0.1%
43.64401926 1
 
< 0.1%
20.46725179 1
 
< 0.1%
41.94521813 1
 
< 0.1%
73.45256112 1
 
< 0.1%
41.91328968 1
 
< 0.1%
1.334472225 1
 
< 0.1%
Other values (13165) 13165
65.8%
(Missing) 1925
 
9.6%
ValueCountFrequency (%)
0 4901
24.5%
9.059177367 × 10-51
 
< 0.1%
0.0003606410336 1
 
< 0.1%
0.001050009258 1
 
< 0.1%
0.001934566193 1
 
< 0.1%
0.002951479598 1
 
< 0.1%
0.00461907666 1
 
< 0.1%
0.004765796956 1
 
< 0.1%
0.008082551429 1
 
< 0.1%
0.01108578518 1
 
< 0.1%
ValueCountFrequency (%)
669.1806354 1
< 0.1%
653.9742401 1
< 0.1%
653.5994148 1
< 0.1%
547.5430146 1
< 0.1%
503.3584616 1
< 0.1%
473.5141884 1
< 0.1%
471.8738072 1
< 0.1%
454.0587844 1
< 0.1%
372.1492654 1
< 0.1%
366.1279206 1
< 0.1%

temp_diff
Real number (ℝ)

Missing 

Distinct18024
Distinct (%)99.8%
Missing1947
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean4.8258364
Minimum-102.68754
Maximum16.948519
Zeros17
Zeros (%)0.1%
Negative859
Negative (%)4.3%
Memory size312.5 KiB
2025-06-08T20:00:23.562980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-102.68754
5-th percentile0.064783169
Q12.9948132
median5
Q37.034932
95-th percentile9.9472973
Maximum16.948519
Range119.63605
Interquartile range (IQR)4.0401188

Descriptive statistics

Standard deviation5.255553
Coefficient of variation (CV)1.089045
Kurtosis247.88394
Mean4.8258364
Median Absolute Deviation (MAD)2.018339
Skewness-12.968018
Sum87120.824
Variance27.620837
MonotonicityNot monotonic
2025-06-08T20:00:23.652633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
0.1%
5 14
 
0.1%
4.380869588 1
 
< 0.1%
1.210889028 1
 
< 0.1%
2.846758719 1
 
< 0.1%
7.793743918 1
 
< 0.1%
6.824157296 1
 
< 0.1%
6.459257973 1
 
< 0.1%
3.523887191 1
 
< 0.1%
5.44775948 1
 
< 0.1%
Other values (18014) 18014
90.1%
(Missing) 1947
 
9.7%
ValueCountFrequency (%)
-102.6875354 1
< 0.1%
-102.0189038 1
< 0.1%
-100.3924088 1
< 0.1%
-100.1048193 1
< 0.1%
-100.0360788 1
< 0.1%
-99.94652896 1
< 0.1%
-99.91779095 1
< 0.1%
-99.70816374 1
< 0.1%
-99.40042955 1
< 0.1%
-98.55538856 1
< 0.1%
ValueCountFrequency (%)
16.94851946 1
< 0.1%
16.33105245 1
< 0.1%
16.12899216 1
< 0.1%
15.52686766 1
< 0.1%
15.1953028 1
< 0.1%
15.15714615 1
< 0.1%
14.61880932 1
< 0.1%
14.56366968 1
< 0.1%
14.55825899 1
< 0.1%
14.51167988 1
< 0.1%

soiled_irradiance
Real number (ℝ)

High correlation  Missing 

Distinct18044
Distinct (%)100.0%
Missing1956
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean150.57722
Minimum-205.90091
Maximum772.93546
Zeros0
Zeros (%)0.0%
Negative407
Negative (%)2.0%
Memory size312.5 KiB
2025-06-08T20:00:23.745251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-205.90091
5-th percentile5.2715591
Q153.113682
median124.71497
Q3224.56236
95-th percentile386.1347
Maximum772.93546
Range978.83637
Interquartile range (IQR)171.44868

Descriptive statistics

Standard deviation122.43643
Coefficient of variation (CV)0.81311393
Kurtosis0.61597188
Mean150.57722
Median Absolute Deviation (MAD)81.926921
Skewness0.91024014
Sum2717015.3
Variance14990.68
MonotonicityNot monotonic
2025-06-08T20:00:23.836807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
209.264932 1
 
< 0.1%
1.24000927 1
 
< 0.1%
111.5002352 1
 
< 0.1%
152.9600748 1
 
< 0.1%
36.27294023 1
 
< 0.1%
304.2861641 1
 
< 0.1%
90.65409663 1
 
< 0.1%
22.28664099 1
 
< 0.1%
113.3925753 1
 
< 0.1%
124.9326719 1
 
< 0.1%
Other values (18034) 18034
90.2%
(Missing) 1956
 
9.8%
ValueCountFrequency (%)
-205.9009104 1
< 0.1%
-172.4255941 1
< 0.1%
-169.6456547 1
< 0.1%
-166.3414282 1
< 0.1%
-163.6260284 1
< 0.1%
-153.4384448 1
< 0.1%
-145.5407317 1
< 0.1%
-141.9649507 1
< 0.1%
-141.887943 1
< 0.1%
-139.8049336 1
< 0.1%
ValueCountFrequency (%)
772.9354645 1
< 0.1%
756.9436507 1
< 0.1%
710.6868742 1
< 0.1%
695.3161789 1
< 0.1%
683.5683944 1
< 0.1%
678.3565703 1
< 0.1%
676.653022 1
< 0.1%
672.0853583 1
< 0.1%
668.8702254 1
< 0.1%
667.968964 1
< 0.1%

has_error
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
14088 
0
5912 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 14088
70.4%
0 5912
29.6%

Length

2025-06-08T20:00:23.925878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T20:00:23.971394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 14088
70.4%
0 5912
29.6%

Most occurring characters

ValueCountFrequency (%)
1 14088
70.4%
0 5912
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 14088
70.4%
0 5912
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 14088
70.4%
0 5912
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 14088
70.4%
0 5912
29.6%

Interactions

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2025-06-08T20:00:06.171583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:07.722149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:09.134212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-08T20:00:13.377929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:14.905433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:16.218443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:17.784294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:19.158444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:59:57.224452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:59:58.568959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:00.090688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:01.566016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:03.240664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:04.802104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:06.255518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-08T20:00:09.212935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:10.496893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:12.057801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:13.469486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-08T20:00:17.860945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:19.243269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:59:57.308431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:59:58.647735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:00.172225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:01.663244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-08T20:00:04.906141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:06.333122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:07.891485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:09.279452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:10.573000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:12.144372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:13.543039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:15.095569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:16.474224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-08T20:00:08.060065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:09.441949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:10.765699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-08T20:00:19.464874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:59:57.569595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:59:58.922556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:00.431679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:02.139600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:03.576207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:05.178003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:06.697090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:08.139603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:09.528205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:10.851189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:12.408691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:13.838339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:15.334692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:16.741289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:18.159737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-08T20:00:24.037142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
cloud_coveragecurrentefficiencyerror_codehas_errorhumidityinstallation_typeirradiancemaintenance_countmodule_temperaturepanel_agepower_outputpressuresoiled_irradiancesoiling_ratiostring_idtemp_difftemperaturevoltagewind_speed
cloud_coverage1.000-0.003-0.0110.0000.0000.0030.014-0.006-0.009-0.002-0.003-0.0080.008-0.0070.0040.0000.0010.001-0.007-0.000
current-0.0031.0000.3180.0000.014-0.0040.0000.424-0.0090.0060.0060.453-0.0090.250-0.0030.000-0.0030.0060.1140.001
efficiency-0.0110.3181.0000.0160.012-0.0800.0080.7010.019-0.057-0.2280.290-0.0070.1410.3550.011-0.011-0.0530.206-0.008
error_code0.0000.0000.0161.0001.0000.0000.0000.0160.0160.0000.0000.0110.0000.0120.0140.0040.0110.0000.0000.000
has_error0.0000.0140.0121.0001.0000.0000.0000.0160.0160.0000.0000.0240.0130.0210.0160.0000.0160.0050.0110.000
humidity0.003-0.004-0.0800.0000.0001.0000.0110.001-0.0020.000-0.008-0.0080.013-0.0030.0010.003-0.0040.001-0.0040.001
installation_type0.0140.0000.0080.0000.0000.0111.0000.0000.0150.0000.0000.0100.0130.0000.0000.0000.0000.0000.0000.008
irradiance-0.0060.4240.7010.0160.0160.0010.0001.000-0.0090.0090.0060.386-0.0080.5820.0050.005-0.0040.0120.270-0.005
maintenance_count-0.009-0.0090.0190.0160.016-0.0020.015-0.0091.0000.003-0.005-0.0040.008-0.0270.0240.0040.009-0.0030.002-0.010
module_temperature-0.0020.006-0.0570.0000.0000.0000.0000.0090.0031.000-0.006-0.002-0.008-0.0010.0050.0030.2330.915-0.003-0.008
panel_age-0.0030.006-0.2280.0000.000-0.0080.0000.006-0.005-0.0061.0000.0080.0020.0050.0010.0000.005-0.0080.0080.012
power_output-0.0080.4530.2900.0110.024-0.0080.0100.386-0.004-0.0020.0081.0000.0010.2260.0030.000-0.012-0.0030.8860.005
pressure0.008-0.009-0.0070.0000.0130.0130.013-0.0080.008-0.0080.0020.0011.000-0.000-0.0050.000-0.002-0.0030.002-0.001
soiled_irradiance-0.0070.2500.1410.0120.021-0.0030.0000.582-0.027-0.0010.0050.226-0.0001.000-0.7360.000-0.007-0.0030.164-0.003
soiling_ratio0.004-0.0030.3550.0140.0160.0010.0000.0050.0240.0050.0010.003-0.005-0.7361.0000.0000.0020.0070.001-0.003
string_id0.0000.0000.0110.0040.0000.0030.0000.0050.0040.0030.0000.0000.0000.0000.0001.0000.0000.0140.0070.004
temp_diff0.001-0.003-0.0110.0110.016-0.0040.000-0.0040.0090.2330.005-0.012-0.002-0.0070.0020.0001.000-0.004-0.009-0.003
temperature0.0010.006-0.0530.0000.0050.0010.0000.012-0.0030.915-0.008-0.003-0.003-0.0030.0070.014-0.0041.000-0.003-0.008
voltage-0.0070.1140.2060.0000.011-0.0040.0000.2700.002-0.0030.0080.8860.0020.1640.0010.007-0.009-0.0031.0000.003
wind_speed-0.0000.001-0.0080.0000.0000.0010.008-0.005-0.010-0.0080.0120.005-0.001-0.003-0.0030.004-0.003-0.0080.0031.000

Missing values

2025-06-08T20:00:19.628654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-08T20:00:19.781746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-08T20:00:20.058558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

temperatureirradiancehumiditypanel_agemaintenance_countsoiling_ratiovoltagecurrentmodule_temperaturecloud_coveragewind_speedpressurestring_iderror_codeinstallation_typeefficiencypower_outputtemp_diffsoiled_irradiancehas_error
id
07.817315576.17927041.24308732.1355014.00.80319937.4035271.96378713.69114762.49404412.8249121018.8665050.03.02.00.56209673.4525615.873832113.3925750
124.785727240.0039731.35964819.9774608.00.47945621.8433150.24147327.54509643.85123812.0120441025.6238543.00.00.00.3964475.2745772.759369124.9326721
246.652695687.61279991.2653681.4964014.00.82239848.2228824.19180043.36370849.7041331.8144001010.9226542.00.02.00.573776202.140687-3.288987122.1210811
353.339567735.14117996.19095518.4915823.00.83752946.2957480.96056757.72043667.3614738.7362591021.8466630.03.00.00.62900944.4701684.380870119.4393900
45.57537412.24120327.49507330.7226976.00.5518330.0000000.8980626.7862633.6320000.5226841008.5559581.00.01.00.3418740.0000001.2108895.4861071
516.545541174.45512386.11683529.8532025.00.9628805.3563202.87806819.39230011.1708748.5414041006.9578802.00.03.00.37612715.4158522.8467596.4757501
618.728064531.96010191.25225420.6720994.00.9118210.7063482.13141126.52180831.7363613.5938301016.0050800.00.01.00.5371961.5055177.79374446.9076431
752.060643420.93935222.0726654.1063203.00.52869413.2285233.29923658.88480059.1358798.3217331030.4971802.01.00.00.46685043.6440196.824157198.3912611
824.180080495.79971148.45598912.0298138.00.68432719.9765733.25572530.63933880.5950451.4004641012.8003752.03.00.00.53860065.0382266.459258156.5108140
937.895969286.7390188.02347234.6217779.00.8357601.6709130.35142349.29745434.3584464.6765841001.3287852.02.03.00.3797710.58719811.40148547.0939611
temperatureirradiancehumiditypanel_agemaintenance_countsoiling_ratiovoltagecurrentmodule_temperaturecloud_coveragewind_speedpressurestring_iderror_codeinstallation_typeefficiencypower_outputtemp_diffsoiled_irradiancehas_error
id
1999031.776213884.57990955.7730120.8668604.00.69766322.5256921.51656440.61690446.9191237.6050481037.1048651.03.02.00.64625434.1616538.840691NaN0
1999133.250663487.69763482.64602134.7817672.00.69766353.4430401.55841337.10668932.20255511.8786491003.2783583.01.02.00.395290NaN3.856026NaN1
1999244.838824632.53947741.4718134.3543170.00.53210029.7986554.13780851.02693524.1555809.6433391007.0187082.02.03.00.558259123.3011206.188111295.9653451
1999325.855841315.8626795.0124689.0153002.00.57173518.5497390.71379329.51079868.44925714.3680071023.6188373.00.00.00.45188413.2406683.654957135.2728211
1999415.629977258.80124758.18878117.4977313.00.6603190.0000000.21571322.6276341000.00000014.9987191022.4969161.02.00.00.4416250.0000006.99765787.9098941
1999516.868428499.65473093.53031814.3939673.00.73891112.1477113.00535526.2068101.73301312.5941221018.3744671.02.03.00.66490736.5081859.338381NaN1
1999653.415061296.97030393.98571425.9970122.00.5130610.0000000.53211965.00000064.5586670.9769911016.0811023.00.01.00.3540700.00000011.584939144.6064221
199972.442727660.32801937.96891832.8183969.00.54860213.0479504.07549811.58486957.7301344.7509371009.6844613.03.03.00.41973453.1768969.142142298.0709470
1999824.720345632.76070043.01470219.0635174.00.6976630.0000001.06890621.14935178.12368911.3041581006.6738750.00.03.00.6619630.000000NaNNaN1
1999925.311591793.74522428.91878120.0731341.00.97192241.2476164.01768432.62307995.8071467.2764221017.3948042.03.01.00.714566165.7198827.31148822.2866410